Automatic Data Acquisition with Robots for Indoor Fingerprinting

This paper addresses the problem of automatic data collection for the purpose of indoor positioning via Received Signal Strength (RSS) fingerprinting. A robotic platform with basic odometer sensors was used in an university building to automate the process of data acquisition which becomes particularly time consuming when considering mapping of large spaces such as shopping malls or hospitals. More than 3000 observations were collected. We associated for each observation their two dimensional coordinates with the received MAC RSS vector. Preprocessing methods included data augmentation and feature normalization. We searched for multiple models and one of the best performance was achieved by using neural networks and post-filtering.